// Consulting — Data Platform & Observability
Data platform & fleet observability for robotics.
Turn the multi-modal data your fleet already generates into something people can actually analyze and troubleshoot.
Who this
is for
Robotics teams that want their fleet data to do more than sit on a hard drive. Yes, debugging is faster when the data is in one place — but the bigger payoff is usually elsewhere: finding patterns across the fleet, spotting where the next optimization is, comparing how a new model version performs against last month’s, giving ops and product teams a view of the operation they don’t have today. Most teams have the data already — camera, LiDAR, ROS bags, telemetry — it’s just scattered, with each team that needs it building its own pipeline.
What we
usually see
Multi-modal data on the robot, multi-modal data in storage somewhere, no clean way to ask a question across both. Engineers write fresh analysis code for every incident. ML training data and operational data each live in their own world and don’t quite agree on what “that run” means. Foxglove or something like it is partially in place but the pipes feeding it were built quickly, by whoever needed it most that quarter, and the shape is starting to show.
Where we
can help
Getting data off the robot reliably — edge capture, on-robot buffering, and selective sync that survives intermittent connectivity.
The storage layer — most teams need some mix of a lake (raw, cheap, multi-modal), a lakehouse (the queryable middle that handles ML and analytics on the same data), and a warehouse (where structured analytics actually lives). We pick the mix based on what the team needs to do with the data, not what a vendor is pitching.
Observability and replay — TraceHouseTM first, plus integration with the rest of what the team already runs.
BI and analysis tooling that marries business and engineering — bringing operational, ML, and product data into the same place so ops, product, and engineering can ask questions of it together.
Training-data infrastructure: capture, sampling, labeling, dataset versioning, eval-set management — the pipeline that feeds the ML side.
Real-time fleet monitoring and alerting, wired into the on-call rotation rather than a Slack channel nobody watches.
Access controls, audit logs, and retention — the parts that need to exist before a regulator or enterprise customer asks.
And more — what’s listed here is a sample, not a menu. Most engagements pull in whatever’s most painful that month.
Not exhaustive. We pick up what the team is already on and bring in alternatives when it’s worth it.
Why teams
work with us
We built TraceHouseTM, our own fleet observability product, because we were the team that needed it. Everything on this page — the lakehouse choices, the edge-to-cloud sync, the audit trail, the way BI tooling plugs into operational data — is something we first had to figure out for ourselves. The architectures we recommend are the ones we’ve shipped, watched scale, and had to debug at 2am when ingestion stopped. Compliance and audit isn’t a separate workstream because the platform we built had to satisfy that from day one.
Adjacent
work
Engagements rarely live alone. A couple of the areas this one most often pulls in.
Agentic AI for Robotics
A queryable data platform is the substrate that makes a conversational ops agent useful. Most teams build the platform first and the agent on top.
Production Engineering
Telemetry is only worth what your release process lets you act on. The data work and the release work share enough wiring to often be a single engagement.
Get in
touch
Reach the team directly. Tell us what you’re trying to ship and we’ll tell you what an engagement on this would look like.
Looking for the broader practice? Back to the consulting overview.